Deep neural network (DNN) models have achieved tremendous success in many tasks.
On the other hand, it is well known that they are intriguingly susceptible to adversarial attacks of different kinds [1]}, thus there is a pressing lack of models that are robust to such attacks. Several mechanisms are proposed as empirical defenses to various known adversarial perturbations. However, these defenses have been later were circumvented by new more aggressive attacks [2]}, [3]}, [4]}, [5]}.
